Bio-Inspired Techniques in Explainable AI for Enhanced Alzheimer's Disease Prediction: A Comprehensive Review

Abstract

This research investigates the use of bio-inspired techniques in explainable AI (XAI) to predict Alzheimer's disease (AD). Alzheimer's disease is a neurological disease that makes early detection difficult. The use of algorithms that are bio-inspired, derived from biological functions, improves the predictive precision of artificial intelligence models. The main goal is to establish an AI system that is open and accessible so that researchers and doctors can understand the process of decision-making that goes into it. The study makes use of a variety of bio-inspired algorithms, including swarm intelligence, neural networks, and genetic algorithms influenced by biological systems. These methods aid in feature selection, model parameter optimization, and improving the predictability of the AI system. In addition to accurately predicting diseases, the study highlights how crucial it is to give reasons for the model's selections in order to build acceptance and confidence among medical professionals. Enhancing Alzheimer's disease detection techniques through the combination of bio-inspired methods and explainable AI could lead to better patient results and early treatment.